class_docstr: Performs a Shifter softplus loss, which modifies with a value of log(2)
docstr: Applies the Activation function
  
  Parameters
  ----------
  node_feats: torch.Tensor
      The node features.
  
  Returns
  -------
  node_feats: torch.Tensor
      The updated node features.
docstr_tok: ["Applies","the","Activation","function","Parameters","--","--","--","--","--","node_feats",":","torch.Tensor","The","node","features",".","Returns","--","--","--","-","node_feats",":","torch.Tensor","The","updated","node","features","."]
code: def forward(self, X):
      """
      Applies the Activation function
  
      Parameters
      ----------
      node_feats: torch.Tensor
          The node features.
  
      Returns
      -------
      node_feats: torch.Tensor
          The updated node features.
  
      """
      node_feats = self.act(X) - self.shift
      return node_feats
  
imports: import torch
  import torch.nn
  from deepchem.models.torch_models.torch_model import TorchModel
  from deepchem.models.losses import L2Loss
label_desc: Use image node_id 2 for calling the Shifted_softplus obj's underlying member method code with example usage: obj.forward(X) and returns: node_feats